class_docstr: Basic tests for Kernel function 
class_docstr_tok: ["Basic","tests","for","Kernel","function"]
docstr: Tests for the effective length in the SE kernel. 
docstr_tok: ["Tests","for","the","effective","length","in","the","SE","kernel","."]
code: def test_compute_std_slack_se(self):
      """Tests for the effective length in the SE kernel."""
      self.report("Tests for std slack in the SE kernel.")
      # The data here are in the order [dim, scale, num_data]
      prob_params = [[2, 1, 10], [3, 2, 0], [10, 6, 13]]
      n = 5
      for prob in prob_params:
          dim_bws = list(np.random.random(prob[0]) * 0.3 + 0.5)
          kern = SEKernel(prob[0], prob[1], dim_bws)
          X_1 = np.random.random((n, prob[0]))
          X_2 = np.random.random((n, prob[0]))
          X_tr = np.random.random((prob[2], prob[0]))
          _, std_1 = self._compute_post_covar(kern, X_tr, X_1)
          _, std_2 = self._compute_post_covar(kern, X_tr, X_2)
          std_diff = np.abs(std_1 - std_2)
          std_slack = kern.compute_std_slack(X_1, X_2)
          diff_12_scaled = kern.get_effective_norm(
              X_1 - X_2, order=2, is_single=False
          )
          kern_diff_12_scaled = (
              kern.hyperparams["scale"] * diff_12_scaled
          )
          assert np.all(std_diff <= std_slack)
          assert np.all(std_slack <= kern_diff_12_scaled)
  
file_path: dragonfly/dragonfly/gp/unittest_kernel.py
imports: from __future__ import absolute_import
  from __future__ import division
  import numpy
  from .kernel import Kernel, SEKernel, MaternKernel, PolyKernel, CoordinateProductKernel
  from ..utils.ancillary_utils import dicts_are_equal
  from ..utils.base_test_class import BaseTestClass, execute_tests
label_desc: Use image node_id 10 for calling the KernelBasicTestCase obj's underlying member method code with example usage: obj.test_compute_std_slack_se() without return types